High false positive rates (type I error) in studies
A non-intentional cause is failing in checking model’s assumptions
Researchers don’t check models -> higher chances of false conclusions
Few tools for model diagnostics of GLMs, GLMMs.
Can you trust your model?
Example count data:
Modeling with Poisson GLMs/GLMMs
UNDER or OVERDISPERSION:
When data has more or less variability than expected by the distribution used for modeling.
DHARMa
“Real” overdispersion:
Abundances vary more that expected by the model, in general.
Heteroscedasticity:
Zero-inflation:
“Real” overdispersion:
Abundances vary more that expected by the model, in general.
Heteroscedasticity:
Abundances variation increases with the environmental gradient.
Zero-inflation:
“Real” overdispersion:
Abundances vary more that expected by the model, in general.
Heteroscedasticity:
Abundances variation increases with the environmental gradient.
Zero-inflation:
More zero abundances than expected by the model.
Too small standard error of estimates -> narrower confidence intervals
Larger chance of type I error: find an effect when it doesn’t exist
Wrong estimates by ignoring other processes (e.g. zero-inflation causes) in your data-generating process.
Missing the opportunity to learn and get more info from your data. Ecological meanings for modeling/understanding unexpected variability?
Detecting dispersion problems with DHARMa
DHARMaScaled quantile residuals -> Simulating from the model
Residuals between 0 and 1 for ANY model complexity or distribution
Interpreted the SAME way:
If your model is correctly specified, i.e. your have the “data-generating process”, scaled quantile residuals will present a uniform “flat” distribution between 0 and 1.
Wrong model
Dispersion = 5.19, p-value = 0.
Wrong model
Dispersion = 1.9, p-value = 0.
Solution
Dispersion = 1.11, p-value = 0.44.
Wrong model
Zero-inflation = 5.16, p-value = 0.
Residual patterns alone will not tell you which is the cause of overdispersion. E.g.:
‘Real’ overdispersion will show significant test for zero-inflation, and vice-versa.
‘Real’ overdispersion and zero-inflation may have significant heteroscedasticity.
Don’t always assume the most complex/complicated model is the correct one!
DHARMa residuals tools to detect them
glmmTMB
Leite et al. in prep. Dispersion tests in GLMMs: a methods comparison and practical guide.
Thank you!
Vielen Dank!
Acknowledgements to Florian Hartig, Max Pichler, and the Theoretical Ecology Lab group